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MRA-based Statistical Learning from Incomplete Rankings Stéphan Clémençon

Abstract : Statistical analysis of rank data describing preferences over small and variable subsets of a potentially large ensemble of items {1,. .. , n} is a very challenging problem. It is motivated by a wide variety of modern applications, such as recommender systems or search engines. However , very few inference methods have been documented in the literature to learn a ranking model from such incomplete rank data. The goal of this paper is twofold: it develops a rigorous mathematical framework for the problem of learning a ranking model from incomplete rankings and introduces a novel general statistical method to address it. Based on an original concept of multi-resolution analysis (MRA) of incomplete rank-ings, it finely adapts to any observation setting, leading to a statistical accuracy and an algorith-mic complexity that depend directly on the complexity of the observed data. Beyond theoretical guarantees, we also provide experimental results that show its statistical performance.
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Submitted on : Tuesday, April 23, 2019 - 4:32:54 PM
Last modification on : Tuesday, October 19, 2021 - 11:14:12 AM


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  • HAL Id : hal-02107459, version 1


Eric Sibony, Stéphan Clémençon, Jérémie Jakubowicz. MRA-based Statistical Learning from Incomplete Rankings Stéphan Clémençon. MRA-based Statistical Learning from Incomplete Rankings Stéphan Clémençon, 2015. ⟨hal-02107459⟩



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